Numerous e-commerce services, such as Apple's iTunes and Amazon.com, recommend music to a user based on the user's purchase history or the purchase histories of other users. However, the user's purchase history may not provide an accurate representation of the user's music preferences in that the purchase history may identify only a very small subset of the user's entire music collection. The recommendations of these e-commerce services could be improved by applying information identifying all songs in the user's music collection and habits or preferences of the user. However, in many cases, these e-commerce services are implemented as pure web sites. As such, it would be difficult for these e-commerce services to obtain information regarding the user's music collection. Further, the user may be uncomfortable with the idea of allowing multiple entities to track information regarding his or her habits or preferences and the music that he or she owns.
Thus, there is a need for a system and method for refining music recommendations provided to a user from one or more e-commerce services based on the user's entire music collection. There is further a need for such a system and method for refining music recommendations that eliminates the need to store information regarding the user's entire music collection in association with each of the e-commerce services.